EEG-based Epileptic Prediction via a Two-stage Channel-aware Set Transformer Network
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911066939719680 |
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| author | Zheng, Ruifeng Chen, Cong Wang, Shuang Liu, Yiming You, Lin Lu, Jindong Zhu, Ruizhe Zhang, Guodao Huang, Kejie |
| author_facet | Zheng, Ruifeng Chen, Cong Wang, Shuang Liu, Yiming You, Lin Lu, Jindong Zhu, Ruizhe Zhang, Guodao Huang, Kejie |
| contents | Epilepsy is a chronic, noncommunicable brain disorder, and sudden seizure onsets can significantly impact patients' quality of life and health. However, wearable seizure-predicting devices are still limited, partly due to the bulky size of EEG-collecting devices. To relieve the problem, we proposed a novel two-stage channel-aware Set Transformer Network that could perform seizure prediction with fewer EEG channel sensors. We also tested a seizure-independent division method which could prevent the adjacency of training and test data. Experiments were performed on the CHB-MIT dataset which includes 22 patients with 88 merged seizures. The mean sensitivity before channel selection was 76.4% with a false predicting rate (FPR) of 0.09/hour. After channel selection, dominant channels emerged in 20 out of 22 patients; the average number of channels was reduced to 2.8 from 18; and the mean sensitivity rose to 80.1% with an FPR of 0.11/hour. Furthermore, experimental results on the seizure-independent division supported our assertion that a more rigorous seizure-independent division should be used for patients with abundant EEG recordings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15364 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EEG-based Epileptic Prediction via a Two-stage Channel-aware Set Transformer Network Zheng, Ruifeng Chen, Cong Wang, Shuang Liu, Yiming You, Lin Lu, Jindong Zhu, Ruizhe Zhang, Guodao Huang, Kejie Signal Processing Artificial Intelligence Machine Learning Epilepsy is a chronic, noncommunicable brain disorder, and sudden seizure onsets can significantly impact patients' quality of life and health. However, wearable seizure-predicting devices are still limited, partly due to the bulky size of EEG-collecting devices. To relieve the problem, we proposed a novel two-stage channel-aware Set Transformer Network that could perform seizure prediction with fewer EEG channel sensors. We also tested a seizure-independent division method which could prevent the adjacency of training and test data. Experiments were performed on the CHB-MIT dataset which includes 22 patients with 88 merged seizures. The mean sensitivity before channel selection was 76.4% with a false predicting rate (FPR) of 0.09/hour. After channel selection, dominant channels emerged in 20 out of 22 patients; the average number of channels was reduced to 2.8 from 18; and the mean sensitivity rose to 80.1% with an FPR of 0.11/hour. Furthermore, experimental results on the seizure-independent division supported our assertion that a more rigorous seizure-independent division should be used for patients with abundant EEG recordings. |
| title | EEG-based Epileptic Prediction via a Two-stage Channel-aware Set Transformer Network |
| topic | Signal Processing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2507.15364 |